Although object detectors have experienced significant gains in performance since the adoption of deep neural networks (DNNs) [1]},
DNNs remain opaque tools with a complex and unintuitive process of decision-making, making them hard to understand, debug and improve.
A number of different explanation techniques offer potential solutions to these issues.
They have already been shown to find biases in trained models [2]}, help debug them [3]}, increase user's trust [4]}.
A popular approach to explanation involves the use of attribution techniques which produce saliency maps [5]}, [6]}, i.e., heatmaps representing the influence different pixels have on the model's decision.
Unfortunately, these techniques have primarily focused on the image classification task [7]}, [8]}, [4]}, [10]}, [11]}, [12]}, with few addressing other problems such as visual question answering [13]}, video captioning [14]}, [15]} and video activity recognition [15]}.
We address the relatively underexplored direction of generating saliency maps for object detectors.
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